Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation. Objective: This scoping review will synthesize current literature and assess the use of various transformer models for neuroimaging segmentation. Methods: A systematic search in major databases, including Scopus, IEEE Xplore, PubMed, and ACM Digital Library, was carried out for studies applying transformers to neuroimaging segmentation problems from 2019 through 2023. The inclusion criteria allow only for peer-reviewed journal papers and conference papers focused on transformer-based segmentation of human brain imaging data. Excluded are the studies dealing with nonneuroimaging data or raw brain signals and electroencephalogram data. Data extraction was performed to identify key study details, including image modalities, datasets, neurological conditions, transformer models, and evaluation metrics. Results were synthesized using a narrative approach. Results: Of the 1246 publications identified, 67 (5.38%) met the inclusion criteria. Half of all included studies were published in 2022, and more than two-thirds used transformers for segmenting brain tumors. The most common imaging modality was magnetic resonance imaging (n=59, 88.06%), while the most frequently used dataset was brain tumor segmentation dataset (n=39, 58.21%). 3D transformer models (n=42, 62.69%) were more prevalent than their 2D counterparts. The most developed were those of hybrid convolutional neural network-transformer architectures (n=57, 85.07%), where the vision transformer is the most frequently used type of transformer (n=37, 55.22%). The most frequent evaluation metric was the Dice score (n=63, 94.03%). Studies generally reported increased segmentation accuracy and the ability to model both local and global features in brain images. Conclusions: This review represents the recent increase in the adoption of transformers for neuroimaging segmentation, particularly for brain tumor detection. Currently, hybrid convolutional neural network-transformer architectures achieve state-of-the-art performances on benchmark datasets over standalone models. Nevertheless, their applicability remains highly limited by high computational costs and potential overfitting on small datasets. The heavy reliance of the field on the brain tumor segmentation dataset hints at the use of a more diverse set of datasets to validate the performances of models on a variety of neurological diseases. Further research is needed to define the optimal transformer architectures and training methods for clinical applications. Continuing development may make transformers the state-of-the-art for fast, accurate, and reliable brain magnetic resonance imaging segmentation, which could lead to improved clinical tools for diagnosing and evaluating neurological disorders.

Green space is any green infrastructure consisting of vegetation. Green space is linked with improving mental and physical health, providing opportunities for social interactions and physical activities, and aiding the environment. The quality of green space refers to the condition of the green space. Past machine learning-based studies have emphasized that littering, lack of maintenance, and dirtiness negatively impact the perceived quality of green space. These methods assess green spaces and their qualities without considering the human perception of green spaces. Domain-based methods, on the other hand, are labour-intensive, time-consuming, and challenging to apply to large-scale areas. This research proposes to build, evaluate, and deploy a machine learning methodology for assessing the quality of green space at a human-perception level using transfer learning on pre-trained models. The results indicated that the developed models achieved high scores across six performance metrics: accuracy, precision, recall, F1-score, Cohen’s Kappa, and Average ROC-AUC. Moreover, the models were evaluated for their file size and inference time to ensure practical implementation and usage. The research also implemented Grad-CAM as means of evaluating the learning performance of the models using heat maps. The best-performing model, ResNet50, achieved 98.98% accuracy, 98.98% precision, 98.98% recall, 99.00% F1-score, a Cohen’s Kappa score of 0.98, and an Average ROC-AUC of 1.00. The ResNet50 model has a relatively moderate file size and was the second quickest to predict. Grad-CAM visualizations show that ResNet50 can precisely identify areas most important for its learning. Finally, the ResNet50 model was deployed on the Streamlit cloud-based platform as an interactive web application.

Body language is a nonverbal communication process consisting of movements, postures, gestures, and expressions of the body or body parts. Body language expresses human feelings, thoughts, and intentions. It also reveals physical and psychological health conditions: abnormal activities inform peoples’ health conditions, facial expressions indicate their emotional states and abnormal body actions convey specific diseases’ external signs and symptoms. We can observe the importance of studying the body language of people with health conditions through many reports in literature written by healthcare (medical) and artificial intelligence researchers. This paper comprehensively reviews artificial intelligence-based articles that have studied patients’ body language. We also conduct different descriptive and exploratory examinations of the findings using data analysis techniques, which provide more authentic domain knowledge of abnormal activities, abnormal body actions, and more precise analysis of methodologies used in machine learning tasks for studying these abnormalities. The paper’s results are essential for developing intelligent automated systems that accurately evaluate patients’ physical and psychological conditions, precisely identify external signs and symptoms of diseases, and adequately monitor patients’ health conditions.

Cardiovascular diseases (CVDs) are group disorders affecting the heart or involving constricted blood arteries. Early disease detection increases the likelihood of survival. As a result, newer methods such as machine learning emerged, capable of processing and analysing vast quantities of complex medical data and providing a more accurate prediction of diseases, including CVDs. However, due to factors such as overfitting and bias, the single classifier could not ensure optimum prediction. Thus, this study proposes a stacking ensemble classifier, which combines several single classifiers to produce an optimal predictive model. The Framingham Heart Study dataset was used to train the machine learning algorithms. The exploratory data analysis indicates that CVD was more common in males and diabetic individuals. Furthermore, individuals above the age of 65 were more susceptible to CVDs. Feature selection, missing value imputation, and data sampling were performed as part of data preprocessing. The results show that the proposed stacked ensemble classifier achieved 88.33% accuracy, 89.95% precision, 86.27% recall and 88.07% F1-score. Furthermore, the significance test results indicate that the proposed model performs significantly better than most models evaluated in this research. Finally, the comparative analysis showed that the proposed ensemble classifier performs better than most studies using the same dataset. The proposed model achieved a high F1-score, indicating it can accurately predict the cases with and without CVD.

Body language refers to the unspoken communication conveyed through human body actions like body movements and postures, limb gestures, and facial and other bodily expressions. It acts as a transparent medium, exposing an individual’s emotions, attitudes, true thoughts, intentions, and physical and mental health states. A person may express pain using hand movements or other bodily cues, their facial expressions potentially offering insights into the intensity of the pain. Additionally, various diseases and pains can induce abnormalities in body movements, postures, and expressions, signaling distress or discomfort. Therefore, investigating the cause-effect relationships between diseases/pains and patients’ abnormal body language holds significant relevance, promising to enhance our understanding and management of these conditions. This importance has been reflected in numerous healthcare and artificial intelligence (AI) research articles. AI studies investigate this and related topics by detecting, recognizing, and analyzing patients’ abnormal activities and body actions using machine-learning techniques. However, most AI studies do not consider comprehensive domain knowledge that describes a complete and accurate list of patients’ abnormal actions caused by a disease or pain. Though these results appear consistent and stable from an AI outlook, they fall short when viewed through the prism of healthcare, primarily because the limited domain knowledge incorporated in the AI studies makes the findings partially incomplete. To overcome these drawbacks, this paper comprehensively reviews healthcare and medical studies centered on patients’ body language from an AI outlook. It presents a thorough descriptive and exploratory analysis of the findings, yielding a more accurate and comprehensive understanding of the causational connections between diseases and abnormal body actions and the strength of the evidence supporting these connections. The analysis enables us to define “disease-to-abnormality” and “abnormality-to-disease” mappings that result in building exhaustive and accurate lists of abnormal body actions induced by diseases and pains as well as lists of diseases and pains causing particular abnormal body actions. The generation of these lists is assisted by the concepts of “correlation strength index” and “strongly correlated selection” defined in this paper. The paper’s results have significant implications for developing machine learning systems that can more accurately analyze patients’ physical and mental health states, correctly identify external signs and symptoms of diseases, and effectively monitor health conditions.